Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gooseworks-ai/goose-skills --skill tam-buildergit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/tam-builder)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/tam-builder"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/tam-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/tam-builder"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/tam-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 146 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 147 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 148 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.01526 |
| Opus 5 | $0.00034 | $0.00763 |
| Sonnet 5 | $0.00014 | $0.00305 |
| Haiku 4.5 | $0.00007 | $0.00153 |
Grade A, and why
tam-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAM Builder
Build and maintain a scored Total Addressable Market. Uses Apollo Company Search to discover companies, scores ICP fit (0-100), assigns tiers (1/2/3), and auto-builds a persona watchlist for Tier 1-2 companies using Apollo People Search (free).
Three modes:
- build — First-time TAM construction from Apollo search
- refresh — Update existing TAM: re-score, detect tier changes, deprecate stale companies
- status — Read-only report of current TAM state
Prerequisites
Apollo API Key
Add to .env:
APOLLO_API_KEY=your-api-key-here
That's it — one env var.
Config Format
Create a JSON config per client/segment:
{
"client_name": "happy-robot",
"tam_config_name": "voice-ai-midmarket",
"company_filters": {
"organization_num_employees_ranges": ["51,200", "201,500", "501,1000"],
"q_organization_keyword_tags": ["call center", "contact center"],
"organization_locations": ["United States"]
},
"scoring": {
"weights": {
"employee_count_fit": 30,
"industry_fit": 25,
"funding_stage_fit": 20,
"geo_fit": 15,
"keyword_match": 10
},
"tier_thresholds": { "tier_1_min_score": 75, "tier_2_min_score": 50 },
"target_industries": ["Telecommunications", "Customer Service"],
"target_employee_ranges": [[51, 200], [201, 500], [501, 1000]],
"target_funding_stages": ["Series A", "Series B", "Series C"],
"target_geos": ["United States"]
},
"watchlist": {
"enabled": true,
"personas_per_company": 3,
"person_filters": {
"person_titles": ["VP of Operations", "Head of Customer Service"],
"person_seniority": ["vp", "director", "c_suite"]
},
"tiers_to_watch": [1, 2]
},
"mode": "standard",
"max_pages": 50
}
Approval Gate
CRITICAL: Never export results without explicit user approval.
Required flow:
- Search Apollo for a small sample first (~100 companies)
- Score them and present: tier distribution, example Tier 1/2 companies, scoring sanity check
- Get explicit user approval before running the full build
- Only then run the full search + score + export
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 157 lines · 69 tokens per session scan A 1cd1baf79b75
tam-builder is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 69 tokens to every session and 1,526 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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